Comparison between the ages for the types (after controlling for gender)¶

Questions

What are the differences between the ages for the different types
Do we observe the same changes as globally?

Age effect - General Questions

What are the differences between the ages?
Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w? Are they the same? Is there a gradient?
Are they different for the two genders?
Are they different for the two types?

Loads¶

Libraries and functions¶

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”

==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data¶

Stats¶

Differentially expressed genes¶

  52w VS 8w (SPF)    52w VS 8w (GF) 104w VS 52w (SPF)  104w VS 52w (GF) 
        0.4335011         0.5316667         0.2890095         0.5009242 
 104w VS 8w (SPF)   104w VS 8w (GF) 
        0.4237078         0.5937668

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”

Counts¶

52w != 8w or 104w != 8w or 104w != 52w for SPF with abs(FC) > 2

52w != 8w or 104w != 8w or 104w != 52w for GF with abs(FC) > 2

Comparison of the numbers per types¶

Differentially expressed genes

Differentially more expressed genes

Differentially less expressed genes

DEG into gene co-expression network¶

White: up-regulated
Black: down-regulated

	52w VS 8w	104w VS 52w	104w VS 8w
SPF
GF

GO analysis¶

Biological process¶

Dot-plot with the most over-represented BP GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

	52w VS 8w	104w VS 52w	104w VS 8w
SPF
GF

52w VS 8w (SPF)

52w VS 8w (GF)

104w VS 52w (SPF)

104w VS 52w (GF)

Cellular components¶

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Molecular functions¶

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

KEGG pathways¶

[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."

Warning message in file.rename(from = paste("mmu", cat, ".pathview.multi.png", sep = ""), :
“cannot rename file 'mmu00533.pathview.multi.png' to '../results/dge/age-effect/age_type/kegg/over_repr_kegg/mmu00533.pathview.multi.png', reason 'No such file or directory'”

[1] "Note: 7 of 5406 unique input IDs unmapped."

[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."

SPF effect in aging¶

Question: Is there any genes that shows an increasing fold change in SPF with aging while not for GF?

	52w vs 8w for SPF	52w vs 8w for GF	104w vs 52w for SPF	104w vs 52w for GF	Gene number
Set 1	!=		!=		244
Set 2	!=	==	!=	==	92

Genes with differential expression with aging in SPF but not in GF (set2)¶

52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) > 0¶

[1] 33

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 23.489, df = 31, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9456141 0.9867280
sample estimates:
      cor 
0.9730381

52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) < 0¶

[1] 23

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -19.401, df = 21, p-value = 6.881e-15
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9887658 -0.9368473
sample estimates:
       cor 
-0.9732205

52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) > 0¶

[1] 24

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -8.1923, df = 22, p-value = 3.971e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9415922 -0.7145938
sample estimates:
       cor 
-0.8678267

52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) < 0¶

[1] 12

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 4.3999, df = 10, p-value = 0.001335
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4459551 0.9453668
sample estimates:
      cor 
0.8120303

	Wald padj < 0.05	LFC > 0 (Wald padj < 0.05)	LFC < 0 (Wald padj < 0.05)
52w VS 8w (SPF)	1391	796	595
52w VS 8w (GF)	600	367	233
104w VS 52w (SPF)	1474	885	589
104w VS 52w (GF)	1623	846	777
104w VS 8w (SPF)	3231	1774	1457
104w VS 8w (GF)	1861	972	889

	52w VS 8w (SPF)	52w VS 8w (GF)	104w VS 52w (SPF)	104w VS 52w (GF)	104w VS 8w (SPF)	104w VS 8w (GF)
0610007P14Rik	NA	NA	NA	0.4582787	NA	0.3508672
0610009B22Rik	NA	NA	NA	0.3173098	NA	0.3199247
0610009L18Rik	NA	NA	NA	NA	-0.7734166	NA
0610009O20Rik	NA	NA	-0.7088350	-0.4522004	-0.4394075	NA
0610010F05Rik	NA	NA	0.5226107	NA	NA	NA
0610010K14Rik	NA	NA	NA	NA	-0.3647340	NA